Agents tooling under the hood

📈 Towards Agentic chatbots

🤖 Demystifying AI Agents

🔍 What are these new tools?

AI Agents are mostly autonomous systems built on LLMs by giving them access to tools that can:

• 🎯 Manage context, e.g. using memory to keep track of previous steps

• 📝 Create detailed plans

• 🛠️ Use various tools (e.g. web search, code execution, data analysis, write reports) to decide to add context

• 🔄 Execute multi-step tasks

• Orchestrate tasks between specialized agents

👨‍💻 What does using an Agentic Chatbot look like?

What are tools in the Agent context?

• Model Context Protocal (MCP) were developed by anthropic to provide additional context to the LLM

• Expose additional information for the LLM to use

• LLMs discover what tools are available and decide how to use them

  • Tools should focus on high-level capability that aligns with a user’s intent, rather then a thin wrapper for an API

  • The metadata associated with each tool—its name, title, description, and inputSchema—forms a “semantic contract” which impact how the LLM uses the tool

• https://modelcontextprotocol.io/docs/getting-started/intro the doc site is an excellent place to start and much of the material is pulled from this source

MCP: Architecture for AI Agents

MCP are made up three parts

1. MCP Host, this is the interface; e.g. could be a chat interface or code

2. MCP client: they interact between the LLM and the tools.

3. MCP server: Tool provides actions and descriptions of capabilities

Tools calling flow

When you submit a query:

1. The client gets the list of available tools from the server

2. Your query is sent to the LLM, lets say Claude, along with tool descriptions

3. Claude decides which tools (if any) to use

4. The client executes any requested tool calls through the server

5. Results are sent back to Claude

6. Claude provides a natural language response

7. The response is displayed to you

Pophive example, chatting with your data

• Pophive (https://www.pophive.org/ ) is a website which aggregates real time data and provides some visualization
• Exploring adding chatbot to allow bespoke data exploration.
• Lots of unknowns for us
  • Where do we host server?
  • How do we scale appropriately while protecting ourselves from cost?
  • Pay per token per user - has the potential to be quite expensive
• For today, we will implement a local MCP server that leans on Claude Desktop and provide tools and resources,eg the data

Lastly let the host now

Since this example uses Claude Desktop

1. update the ~/Library/Application Support/Claude/claude_desktop_config.json file with the following

2. This gives claude access to a new tool

{
  "mcpServers": {
    "data-visualization": {
      "command": "/Users/mad265/git-pub/dissc-agent-tooling/tasks/data-visualization-mcp/venv/bin/python",
      "args": [
        "/Users/mad265/git-pub/dissc-agent-tooling/tasks/data-visualization-mcp/data_viz_server.py"
      ],
      "cwd": "/Users/mad265/git-pub/dissc-agent-tooling/tasks/data-visualization-mcp",
      "env": {
        "DATA_DIR": "/Users/mad265/git-pub/dissc-agent-tooling/data"
      }
    }
  }
}